Скачать или смотреть Static Detection of Silent Misconfigurations with Deep Interaction Analysis

The behavior of large systems is guided by their configurations: users set parameters in the configuration file to dictate which corresponding part of the system code is executed. However, it is often the case that, although some parameters are set in the configuration file, they do not influence the system runtime behavior, thus failing to meet the user’s intent. Moreover, such misconfigurations rarely lead to an error message or raising an exception. We introduce the notion of silent misconfigurations which are prohibitively hard to identify due to (1) lack of feedback and (2) complex interactions between configurations and code.
This paper presents ConfigX, the first tool for the detection of silent configurations. The main challenge is to understand complex interactions between configuration and the code that is affected by them. Our goal is to derive a specification describing non-trivial dependencies between the configuration parameters that lead to silent misconfigurations. To this end, ConfigX uses static analysis to determine which parts of the system code are associated with configuration parameters. ConfigX then infers the connections between configuration parameters by analyzing their associated code blocks. We design customized control- and data-flow analysis to derive a specification of configurations. Additionally, we conduct reachability analysis to eliminate spurious rules to reduce false positives. Upon evaluation on five real-world datasets across three widely-used systems, Apache, vsftpd, and PostgreSQL, ConfigX detected more than 2200 silent misconfigurations. We additionally conducted a user study where we ran ConfigX on misconfigurations reported on user forums by real-world users. ConfigX easily detected issues and suggested a repair for those misconfigurations. Our solutions were accepted and confirmed in the interaction with the users, who originally posted the problems.

Presented at OOPSLA 2021, part of SPLASH 2021.
By Jialu Zhang, Ruzica Piskac, Ennan Zhai, Tianyin Xu